Duration modeling for English letters embedded in Chinese speech

Estimation for the number of components in a mixture model using stepwise split-and-merge EM algorithm

Alzheimer’s disease is the most common form of dementia and the early detection is essential to prevent its proliferation. Real data available has been of paramount importance in order to achieve progress in the automatic detection despite presenting two major challenges: Multi-source observations containing Magnetic resonance (MRI), Positron emission tomography (PET) and Cerebrospinal fluid data (CSF); and also missing values within all these sources. Most machine learning techniques perform this predictive task by using a single data modality. Nevertheless, the integration of all these sources of evidence could possibly bring a higher performance at different stages of disease progression. The Expectation Maximization (EM) algorithm has been successfully employed to handle missing values, but it is not designed for typical Machine Learning scenarios where an imputation model is created over training data and subsequently applied on a testing set. In this work, we propose EMreg-KNN, a novel supervised and multi-source imputation algorithm. Based on the EM algorithm, EMreg-KNN builds a regression ensemble model for the imputation of future data thus allowing the further utilization of any vector-based Machine Learning method to automatically assess the Alzheimer’s disease diagnosis. Using the ADNI database, the proposed method achieves significant improvements on F1, AUC and Accuracy measures over classical imputation methods for this database using four classification algorithms. Considering these classifiers in four different classification scenarios, our algorithm is experimentally superior in terms of the F measure, in nearly 82% of the cases under evaluation.

Within the context of emission tomography, we study volumetric reconstruction methods based on the Expectation Maximization (EM) algorithm. We show, for the first time, the equivalence of the standard implementation of the EM‐based reconstruction with an implementation based on hardware‐accelerated volume rendering for nearest‐neighbor (NN) interpolation. This equivalence suggests that higher‐order kernels should be used with caution and do not necessarily lead to better performance. We also show that the EM algorithm can easily be adapted for different lattices, the body‐centered cubic (BCC) one in particular. For validation purposes, we use the 3D version of the Shepp‐Logan synthetic phantom, for which we derive closed‐form analytical expressions of the projection data. The experimental results show the theoretically‐predicted optimality of NN interpolation in combination with the EM algorithm, for both the noiseless and the noisy case. Moreover, reconstruction on the BCC lattice leads to superior accuracy, more compact data representation, and better noise reduction compared to the Cartesian one. Finally, we show the usefulness of the proposed method for optical projection tomography of a mouse embryo.

In Recent days Semi supervised image segmentation techniques play a noteworthy role in image processing. Semi supervised image segmentation needs both labeled data and unlabeled data. It means that a Small amount of human assistance or Prior information is given during clustering process. This paper discusses an enhanced semi supervised image segmentation method from labeled image. It uses both a background selection marker and fore ground object selection marker separately. The EM (Expectation Maximization) algorithm is used for clustering along with must link constraints. The proposed method is applied for natural images using MATLAB 7. Thus the proposed method extracts Object of Interest (OOI) from OONI (Object of Not Interest) efficiently and the experimental results are compared with Standard K Means and EM Algorithm also. The results show that the proposed system gives better results than the other two methods. It may also be suitable for object extraction from natural images and medical image analysis.

An image fusion method is proposed based on a kind of estimation theory EM (expectation maximization) algorithm and discrete wavelet frame for merging multiple sensor images. The registered images are firstly decomposed by discrete wavelet frame. Secondly since the fusion of the lowest frequency band is crucial to the final result, they are fused effectively by EM algorithm. The high frequency bands are fused by selection method involving the informative importance measure. Then the fused image is achieved by the discrete wavelet frame invert transform. Experimental results indicate that the proposed method outperforms the discrete wavelet transform and the existing image fusion methods.

Electronic health records (EHRs) are the records containing the patients' clinic information. The EHRs have been widely used in disease diagnosis and therapy due to the numerous and valuable medical information in them. However, the missing data problem of EHRs hinders the usage. Replacing the missing data with mean values is an approach of data imputation. But, that method weakens the feature importance. In this study, we use the expectation-maximization (EM) algorithm to impute the missing data in EHRs. Some machine learning models, including artificial neural network, logistic regression, support vector machine, and random forests are used to evaluate the effectiveness of data imputation. The experimental results show that the prediction accuracies of cancers by using those models on the EHRs imputed by EM algorithm are higher than those by mean values, which indicates the EM algorithm is able to provide accurate estimations in data imputation of EHRs.

The goal of this project was to develop two age appropriate atlases (neonatal and one year old) that account for the rapid growth and maturational changes that occur during early development. Tissue maps from this age group were initially created by manually correcting the resulting tissue maps after applying an expectation maximization (EM) algorithm and an adult atlas to pediatric subjects. The EM algorithm classified each voxel into one of ten possible tissue types including several subcortical structures. This was followed by a novel level set segmentation designed to improve differentiation between distal cortical gray matter and white matter. To minimize the req uired manual corrections, the adult atlas was registered to the pediatric scans using high -dimensional, symmetric image normalization (SyN) registration. The subject images were then mapped to an age specific atlas space, again using SyN registration, and the resulting transformation applied to the manually corrected tissue maps. The individual maps were averaged in the age specific atlas space and blurred to generate the age appropriate anatomical priors. The resulting anatomical priors were then used by the EM algorithm to re-segment the initial training set as well as an independent testing set. The results from the adult and age-specific anatomical priors were compared to the manually corrected results. The age appropriate atlas provided superior results as compared to the adult atlas. The image analysis pipeline used in this work was built using the open source software package BRAINSTools.

In the scope of image processing expectation maximization (EM) algorithm takes conspicuous place among the other clustering techniques. EM algorithm is suitable for multidimensional data but it requires a number of clusters to be defined a priori that might be a problem for particular applications. The main aim of this paper is to provide time effective EM clustering modification in the case of the unknown number of clusters and multidimensional input. Our work is based on statistical histogram based expectation maximization algorithm (SHEM) proposed by Yang and Huang with the predefined number of clusters. This method utilizes the histogram to provide EM iterations. However, the estimation of the histogram becomes time consuming task with the increase of input data dimension. Our algorithm extends the use of SHEM algorithm by means of a hierarchical histogram data structure, which allows us to reduce the computational load in the multidimensional case as well as to provide an initialization in the case of the unknown number of clusters. The paper includes several experimental results demonstrating the advantages and the disadvantages of the proposed solution

Nursery rhymes play an important role in young children as they help develop their English language. This study investigated the teachers’ perceptions on the use of lyrics and animation of the nursery rhymes in helping pre-schoolers to learn vocabulary skills and spelling of English words as well as improving their grammar knowledge. The study employed a survey design among 48 kindergarten teachers using a Google link form. The preliminary findings indicated that generally the teachers agree that nursery rhymes should be used in the teaching of English as the initial results indicated that preschool children’s acquisition of new words and grammar was significant using nursery rhymes. This shows that nursery rhymes not only motivate pre-schoolers to learn the language but also create a platform to encourage them to be more interactive while listening to the rhymes.

Mycophenolic acid (MPA), the active metabolite of mycophenolate mofetil (MMF), is widely used in the treatment of systemic lupus erythematosus (SLE). It has been shown that its therapeutic drug monitoring based on the area under the curve (AUC) improves treatment efficacy. MPA exhibits a complex bimodal absorption, and a double gamma distribution model has been already proposed in the past to accurately describe this phenomenon. These previous population pharmacokinetics models (POPPK) have been developed using iterative two stage Bayesian (IT2B) or non-parametric adaptive grid (NPAG) methods. However, non-linear mixed effect (NLME) approaches based on stochastic approximation expectation-maximization (SAEM) algorithms have never been published so far for this particular model. The objectives of this study were (i) to implement the double absorption gamma model in Monolix, (ii) to compare different absorption models to describe the pharmacokinetics of MMF, and (iii) to develop a limited sampling strategy (LSS) to estimate AUC in pediatric SLE patients. A data splitting of full pharmacokinetic profiles sampled in 67 children extracted either from the expert system ISBA (n = 34) or the hospital Saint Louis (n = 33) was performed into train (75%) and test (25%) sets. A POPPK was developed for MPA in the train set using a NLME and the SAEM algorithm and different absorption models were implemented and compared (first order, transit, or simple and double gamma). The best limited sampling strategy was then determined in the test set using a maximum-a-posteriori Bayesian method to estimate individual PK parameters and AUC based on three blood samples compared to the reference AUC calculated using the trapezoidal rule applied on all samples and performances were assessed in the test set. Mean patient age and dose was 13years old (5-18) and 18.1mg/kg (7.9-47.6), respectively. MPA concentrations (764) from 107 occasions were included in the analysis. A double gamma absorption with a first-order elimination from the central compartment best fitted the data. The optimal LSS with samples at 30min, 2h, and 3h post-dose exhibited good performances in the test set (mean bias - 0.32% and RMSE 21.0%). The POPPK developed in this study adequately estimated the MPA AUC in pediatric patients with SLE based on three samples. The double absorption gamma model developed with the SAEM algorithm showed very accurate fit and reduced computation time.

Weakly supervised semantic segmentation based on EM algorithm with localization clues

ABSTRACTThe original purpose of this study was to address the test‐disclosure‐related need to introduce more Graduate Record Examinations (GRE) General Test editions each year than formerly, in a context of stable, or possibly declining examinee volume. The legislative conditions that created this initial concern regarding test equating have abated. However, several of the test equating models considered in this research might provide other advantages to the GRE Program. These potential advantages are listed in the body of the report.Equating can be considered to consist of three parts: (1) a data collection design, (2) an operational definition of the equating transformation, and (3) the specific statistical estimation techniques used to obtain the equating transformation. Currently, the GRE General Test collects data using an equivalent groups design. Typically, a linear equating method is used, and the specific estimation technique is setting means and standard deviations equal.For this research, two other data collection designs were studied: nonrandom group, external anchor test, and random group, preoperational section. Both item response theory (IRT) and linear equating definitions were used. IRT true score equating was based on item statistics for the three‐parameter logistic model as estimated using LOGIST. Linear models included section pre‐equating using the EM algorithm, Tucker's observed score model, and several true score models developed by Tucker and Levine. For each of the three GRE measures, verbal, quantitative and analytical, all equating methods were assessed for bias and root mean squared error by equating a test edition to itself through a chain with six equating links.Bias and root mean squared error were extremely large for equating the verbal and analytical measures using section pre‐equating or IRT equating with data based on the random group preoperational section data collection design. For the quantitative measure, this data collection design produced a small amount of bias, but moderate amount of root mean squared error.Using the nonrandom group, external anchor test data collection design, quantitative equatings had moderate amounts of both bias and root mean squared error. Verbal nonrandom group, external anchor test equatings showed relatively small amounts of bias and root mean squared error, with the Tucker observed score model performing particularly well. Bias was small for the analytical anchor test equatings, and root mean squared error ranged from small to moderate.All nonrandom group, external anchor test methods worked about as well in practice for the verbal measure as the currently used random group method does in theory. The current random group method, however, has never been subjected to an empirical check comparable to that used in this study for the experimental equating methods. Two anchor test methods, Tucker 2 True and Levine, appear to have worked as well in practice for the analytical measure as the random group method does in theory.A possible explanation for the generally poor results for the random group, preoperational section data collection design based equatings was the constant use of the last section of the test to collect equating data. It may be, now that the sections of the GRE General Test are administered in various orders in different editions of the test, that the extreme bias found in this study for the verbal and analytical random group preoperational section equatings will disappear or at least be substantially diminished.

The Probabilistic RBF (PRBF) network constitutes an adaptation of the RBF network for classification. Moreover it extends the typical mixture model by allowing the sharing of mixture components among all classes, in contrast to the conventional approach that suggests mixture components describing only one class. The typical learning method of PRBF for a classification task employs the Expectation – Maximization (EM) algorithm. This widely used method depends strongly on the initial parameter values. The Greedy EM algorithm is a recently proposed method that tries to overcome this drawback, in the case of the density estimation problem using mixture models. In this work we propose a similar approach for incremental training of the PRBF network for classification. The proposed algorithm starts with a single component and incrementally adds more components. After convergence the algorithm splits all the components of the network. The addition of a new component is based on criteria for detecting a region in the data space that is crucial for the classification task. Experimental results using several well-known classification datasets indicate that the incremental method provides solutions of superior classification performance.

We consider here the problem of Base Noun Phrase translation. We propose a new method to perform the task. For a given Base NP, we first search its translation candidates from the web. We next determine the possible translation(s) from among the candidates using one of the two methods that we have developed. In one method, we employ an ensemble of Naive Bayesian Classifiers constructed with the EM Algorithm. In the other method, we use TF-IDF vectors also constructed with the EM Algorithm. Experimental results indicate that the coverage and accuracy of our method are significantly better than those of the baseline methods relying on existing technologies.

With the improvement of the ridge regression and EM algorithm, the application of it in the prediction and analysis of concrete compressive strength has attracted more and more attention, and with the introduction of various new methods and theories, the EM algorithm and ridge regression parameters the application of estimation in algorithms is becoming more and more extensive. The purpose of this paper is to study the prediction of concrete compressive strength based on improved EM algorithm and ridge regression algorithm. In this paper, the principles of EM algorithm and ridge regression algorithm are described, the improved EM algorithm and ridge regression algorithm are proposed, and the prediction effect of the algorithm is tested. The experimental results show that when the real value of concrete compressive strength is 37.9mpa, the predicted value before algorithm optimization is 37.42mpa and the predicted value after algorithm optimization is 39.65mpa; Before optimization, the maximum absolute error reached 1.36mpa, and after optimization, the maximum absolute error was 0.37mpa. It can be seen that the improved EM algorithm and ridge regression algorithm have higher accuracy in predicting concrete compressive strength, and the error between them and the real value is the smallest.